Interpreting Aristotle’s <i>Posterior Analytics</i> in Late Antiquity and Beyond
Bibliographic record
Abstract
Part I CONCEPT FORMATION IN POSTERIOR ANALYTICS II 19 1. The Ancient Commentators on Concept Formation Richard Sorabji 2. Proclus' Criticism of Aristotle'sTheory of Abstraction and Concept Formation in Analytica Posteriora II 19 Christoph Helmig 3. Eustratius' Comments on Posterior Analytics II 19 Katerina Ierodiakonou 4. Roger Bacon on Experiment, Induction and Intellect in his Reception of Analytica Posteriora II 19 Pia A. Antolic-Piper Part II METAPHYSICS AS A SCIENCE 5. Alexander of Aphrodisias on the Science of Ontology Maddalena Bonelli 6. Les Seconds Analytique' dans le commentaire de Syrianus sur la Metaphysique d'Aristote Angela Longo Part III DEMONSTRATION, DEFINITION AND CAUSATION 7. Alexander and Philoponus on Prior Analytics I 27-30: Is There Tension between Aristotle's Scientific Theory and Practice? Miira Tuominen 8. Two Traditions in the Ancient Posterior Analytics Commentaries Owen Goldin 9. Aristotle and Philoponus on Final Causes in Demonstrations in Posterior Analytics II 11 Mariska Leunissen 10. Aristotle on Causation and Conditional Necessity: Analytica Posteriora II 12 in Context Inna Kupreeva
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".